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Abstracts published in Epidemiology have been reviewed by the organizations of Epidemiology. Affliate Societies at whose meetings the abstracts have been accepted for presentation. These abstracts have not undergone review by the Editorial Board of Epidemiology.

Background and Objective:

Animal studies have suggested that fine particulate matter (PM) can translocate from the upper respiratory tract to the brain and cause brain inflammation. Brain inflammation is involved in the pathogenesis of neurodegenerative diseases. Hypothesizing therefore that long-term exposure to fine PM might contribute to the development of Alzheimer's disease (AD), the objective of this study is to investigate the association between exposure to fine PM and mild cognitive impairment (MCI) which is associated with a high risk of progression to AD.

Methods:

A study group of 399 women aged 68-79 years who lived for more than 20 years at the same residential address has been assessed for long-term exposure to PM and tested for MCI. The exposure assessment comprised background concentration of PM10 and traffic-related air pollution indicated by the distance of the residential address to the next busy road with a traffic density of more than 10,000 cars per day. The women were assessed for MCI by a battery of several neuropsychological tests and a test for their odour identification ability. Current emotional state was tested using CES Depression Scale. Information on other potential confounders (chronic diseases, smoking, indoor sources of air pollution, educational level) was provided by standardized interview.

Results:

Consistent effects of traffic-related air pollution exposure on test performances were found, but no effects of PM10 background exposure. The test scores were significantly reduced if the participant's residential address was within a distance range of 50 meters to the next busy road. Also, a dose-response relation between distance to road traffic and test performance was observed. The associations were adjusted for potential confounders using regression analysis.